miRNA cargo in circulating vesicles from neurons is altered in individuals with schizophrenia and associated with severe disease

While RNA expression appears to be altered in several brain disorders, the constraints of postmortem analysis make it impractical for well-powered population studies and biomarker development. Given that the unique molecular composition of neurons are reflected in their extracellular vesicles (EVs), we hypothesized that the fractionation of neuron derived EVs provides an opportunity to specifically profile their encapsulated contents noninvasively from blood. To investigate this hypothesis, we determined miRNA expression in microtubule associated protein 1B (MAP1B)–enriched serum EVs derived from neurons from a large cohort of individuals with schizophrenia and nonpsychiatric comparison participants. We observed dysregulation of miRNA in schizophrenia subjects, in particular those with treatment-resistance and severe cognitive deficits. These data support the hypothesis that schizophrenia is associated with alterations in posttranscriptional regulation of synaptic gene expression and provides an example of the potential utility of tissue-specific EV analysis in brain disorders.

In addition to the manufacturer's antibody validation, we further demonstrated the competency of our protocol in the context of anti-MAP1B coupled magnetic beads to recover RNA from putative neuronal-origin extracellular vesicles (EVs) in human serum.Firstly, we tested whether pre-clearing serum affected RNA recovery.Specifically, serum was incubated with naked beads (nil anti-MAP1B) to allow non-specific binding of serum proteins to be retained on-bead and supernatant (pre-cleared serum) recovered.Then, using fresh antibody-coupled magnetic beads incubated with pre-cleared serum or serum that was not pre-cleared demonstrated equivalent RNA recovery (7.9ng/µL and 8.0ng/µL, respectively) (determined by NanoDrop), suggesting non-specific binding to protein G magnetic beads had no effect on RNA recovery from neuronalorigin serum EVs.Secondly, we observed >15-fold recovery of RNA when comparing our protocol with and without anti-MAP1B (fig.S5), suggesting the antibody is fit for purpose.
Small RNA sequencing Small RNA libraries were prepared using the SMARTer smRNA-Seq Kit for Illumina, according to the manufacturer's instructions.For each sample, 7µL of total RNA was polyadenylated via the addition of 3µL Polyadenylation Master Mix and incubation at 16°C for 5 min, after which samples were cooled at 4°C for 1 min.For reverse transcription, 1µL of 3´ smRNA dT primers was added and samples were incubated at 72°C for 3 min then cooled at 4°C for 2 min, followed by addition of 9µL Reverse Transcription Master Mix and incubated with the following program: 42°C 1 hour, 70°C 10 min, 4°C hold.Library amplification and addition of SMARTer unique dual indexes (UDI) was performed by adding 76µL of PCR Master Mix, 4µL of each UDI and incubating samples under the following conditions: 98°C for 1 min.17 cycles of: 98°C for 10 sec.60°C for 5 sec.68°C for 10 sec.

4°C final hold.
Library purification was performed with a NucleoSpin Gel and PCR Clean-Up kit as per the manufacturer's instructions.Briefly, PCR reactions were combined with 2 volumes (i.e.200µL) Buffer NTI, transferred to a DNA-binding NucleoSpin Gel and PCR Clean-up column and bound to the silica membrane via centrifugation (1 min, 3000 x g).The membrane was then washed twice with 4mL Buffer NT3 and dried via centrifugation (10 min, 3000 x g).To elute cDNA, 30µL Buffer NE was added, after which samples were incubated at 70°C for 5 min and centrifuged (2 min, 3000 x g).All libraries were subsequently quantified using Agilent High Sensitivity DNA chips and a KAPA Library Quantification Kit for Illumina Platforms, pooled in equimolar ratios and volumes and size selected via 8% native acrylamide PAGE.The gel was incubated with SYBR gold nucleic acid gel stain and bands of ~175bp, corresponding to microRNA, were excised.Size selected cDNA was eluted and quantified using Agilent High Sensitivity DNA chips and a KAPA Library Quantification Kit.Sequencing libraries, prepared according to the NovaSeq XP workflow, were diluted to a final loading concentration of 200pM using 10mM Tris-HCl pH 8.5 and denatured with 0.2N NaOH.Paired-end sequencing for 101 cycles was performed using the NovaSeq6000 platform.
Raw small RNA bcl sequencing files were demultiplexed and converted to fastq format using bcl2fastq (v.2.20; Illumina) with automatic adapter trimming disabled.Data quality was assessed using FastQC (v0.11.8).Cutadapt (v2.6) was used to remove sequencing adapters and trim the reads of low quality bases.Specifically, 3 nucleotides and 14 nucleotides were removed from the 5' end of read 1 and 2, respectively for all reads.Poly-A tails from the 3' end of read 1 and poly-T tails from the 5' end of read 2 were removed, and any remaining low-quality reads were removed from the 3' ends of reads, using a threshold Phred33 score of 28.Additionally, using NGmerge (v0.3) in adapter-removal mode, 3' overhangs and unmatched bases were removed.To ensure good quality reads >10bp long, a final trimming (Cutadapt) step, as described above, was repeated.Reads were next aligned to the human (Homo sapiens) reference genome (GRCh38 assembly, NCBI) with bowtie2 (v2.4.1) default settings and sorted and indexed with SAMtools.Next, reads were counted with HTSeq (v0.9.1), using the human (Homo sapiens) reference (hsa.gff3)from miRbase v22.1 to define mature miRNA features.HTSeq was run in "union" mode, its strandedness option was set to forwards, and a minimum mapping quality score threshold of 1 was used.Unaligned and ambiguous reads were removed, and count files were merged into a single matrix, with rows representing miRNAs and columns representing samples.The count matrix was imported into R and analysed for differential expression with EdgeR (v3.34.0) (90).Four differential expression comparisons were made: all schizophrenia cases vs. all comparison subjects; cognitive deficit subtype of schizophrenia vs. all comparison subjects; cognitive spared subtype of schizophrenia vs. all comparison subjects and cognitive deficit subtype of schizophrenia vs. cognitive spared subtype of schizophrenia.The count matrix was filtered to only retain libraries that summed to at least 1000 counts, followed by filtering to retain miRNAs that reached 10 counts per million (CPM) in the smallest group (221 schizophrenia cases).Note, to keep analyses consistent, we used this filtering step for both the full cohort analysis and the subtype analyses.Data was then normalised and dispersions were estimated using edgeR's calcNormFactors and estimateDisp functions, respectively.At each stage, MDS and BCV plots were used to visually inspect the data for quality control issues.Finally, differential expression was assessed using edgeR's likelihood ratio test method.

Figure S1 .
Figure S1.Heat map of neuronal origin miRNA consistently observed across samples.The expression of each miRNA (n=105) in each sample (n=477) is represented by a square.Yellow = high expression, blue = low expression.Sample labels in the heat map are omitted due to the large number of samples.Labels for miRNA in the heat map are limited to those miRNA identified from the enrichment analysis (tableS2), irrespective of psychiatric phenotype, and include miR-17 cluster, let-7 family and miR-29 family.miRNA expression is normalised counts per million, converted to z-score.Heat map constructed by ordering the distance matrix to minimize the Hamilton path length.

Figure S2 .Figure S3 .Figure S4 .
Figure S2.Principal component analysis of neuronal origin miRNA from serum extracellular vesicles.Expression of miRNA (normalised counts per million, scaled) in the full cohort (n=477) reveals (A) hsa-miR-19b-3p and hsa-miR-19a-3p explain the most variation in dimension one, represented as the highest quality variables on the factor map by cos2 (squared coordinates).Given that hsa-miR-19b-3p and hsa-miR-19a-3p are transcribed from the same primary transcript and show evidence of correlated expression (tableS8), we repeated PCA (B) without hsa-miR-19a-3p and then (C) without hsa-miR-19b-3p, in each case showing a small reduction in proportion of variance retained in dimension one.

Figure S6 .Figure S7 .
Figure S6.Processing small RNA sequencing data.(A,B) Multidimensional scaling (MDS) plots for miRNA before filtering, (C,D) after filtering to remove low count reads and (E,F) following normalization.(A,C,E) Left panels are miRNA leading log fold changes between each pair of samples with respect to schizophrenia (brown) versus comparison subjects (blue) while (B,D,F) right panels are miRNA leading log fold changes between each pair of samples with respect to cognitive deficit subtype schizophrenia (khaki) cognitive spared subtype schizophrenia (brown) and comparison subjects (blue).SZ=schizophrenia, CO=comparison, szCD= cognitive deficit subtype schizophrenia, szCS= cognitive spared subtype schizophrenia.

Figure S8 miRNA target
Figure S8 miRNA target gene-set association with schizophrenia risk.QQ plots of residualized genic Z values do not indicate that association of excitatory and inhibitory sets with schizophrenia (SZ) was driven by only a subset of genes in the set.(A) Targets of miRNA differentially expressed in SZ compared to non-psychiatric comparison (CO) subjects.(B) Targets of miRNA differentially expressed in schizophrenia subjects with severe cognitive deficits (CD) compared to schizophrenia subjects with spared cognition (CS).(C) Targets of miRNA differentially expressed in CD compared to CO subjects.Related to Table 2 and Fig.3.

Figure S9
Figure S9 Cognitive deficit associated miRNA pathways.Original image from pathways analysis using Consensus Path Database, with edges filtered for minimum relative overlap 0.08.Related to Fig. 4

Fig. S10
Fig. S10 Consistently dysregulated miR-1246 target genes.Predicted targets of hsa-miR-1246 were determined with TargetScan and filtered to include high confidence genes based on binding site efficacy; cumulative weighted context score less than -0.2.(A) Target genes are specifically enriched in the brain and (B) significantly overlap with miR-137 targets.

Table S3 . miRNA expression in subtypes of schizophrenia.
Treatment resistant schizophrenia (TRS): taking clozapine at the time of assessment, n=42.Non-TRS: schizophrenia subjects not taking clozapine at the time of assessment, n=179.Non-psychiatric comparison subjects, n=256.Early onset schizophrenia (EOS): onset before 18 years of age, n=31.Non-EOS: onset at 18 years of age and onwards, n=190.FDR=false discovery rate adjusted P value.

Table S4 . Gene and disease ontologies enriched for genes targeted by miRNA that are dysregulated in schizophrenia subjects compared to comparison subjects
. n=the number of annotated genes from the input list, %=n/total number of genes from the input list (total 715).

Table S5 . Gene and disease ontologies enriched for genes targeted by miRNA that are dysregulated in schizophrenia subjects with cognitive deficit compared to comparison subjects
. n=the number of annotated genes from the input list, %=n/total number of genes from the input list (total 6245).

Table S6 . Gene and disease ontologies enriched for genes targeted by miRNA that are dysregulated in schizophrenia subjects with cognitive deficit compared to cognitively spared schizophrenia subjects
. n=the number of annotated genes from the input list, %=n/total number of genes from the input list (total 2958).

Table S7 . Gene and disease ontologies enriched for genes targeted by miRNA that are dysregulated in subjects with treatment resistant schizophrenia compared to schizophrenia subjects not treatment resistant
. n=the number of annotated genes from the input list, %=n/total number of genes from the input list (total 5911).

Table S8 . Gene and disease ontologies enriched for genes targeted by miRNA that are dysregulated in subjects with treatment resistant schizophrenia compared to not treatment resistant subjects, adjusted for case comparison status
(84)the number of annotated genes from the input list, %=n/total number of genes from the input list (total 6079).TableS9Gene set analysis.Multi-marker Analysis of GenoMic Annotation (MAGMA)(84)identifies significant association between genomic risk for schizophrenia and predicted target genes of dysregulated miRNA.SZ=schizophrenia, CO=non-psychiatric comparison, CD=cognitive deficit subtype schizophrenia, CS=cognitive spared subtype schizophrenia.Title: CNS expression and previous association with schizophrenia for differentially expressed miRNA.c.Description: Manual annotation of differentially expressed miRNA with respect to human CNS expression and previous association with schizophrenia.Demographics and characteristics of individual subjects.c.Description: Detailed characteristics of subjects (n=477), including diagnosis, age of onset, antipsychotic medications and symptom scores.